Developing a computational high-resolution scATAC-seq platform to prioritize non-coding genetic variants

A genome-wide association study (GWAS) is a standard approach for understanding the relationship between genetic variants and disease, resulting in a better understanding of disease mechanisms. However, despite the vast contribution of GWAS to our knowledge and understanding of human biology, our ability to interpret these findings has not yet been fully developed. This is primarily because most disease-disposed regions are found within the non-coding regions of the genome, which affect regulatory elements such as enhancers and promoters. There is currently no clear path to identify causal regulatory SNPs and to link these to the genes and cell types they affect. This thesis work focuses on first understanding scATAC-seq as a technology and its outputs and then taking advantage of its potential power to prioritise non-coding genetic variants and cell types. I have deeply reviewed and explored single-cell open chromatin epigenetics, which led to discovering the effect of the Tn5 cutting pattern on providing different levels of information from scATAC-seq. I developed a mathematical strategy to remove nucleosomal background from scATAC-seq data, increasing the data resolution and enabling the accurate identification of TF-bound regions. We successfully built an analytical platform, Avocato, which provides a complete solution to understand the relationship between non-coding genetic variants and their affected diseases. Avocato is not only capable of analysing and visualising scATAC-seq data but also removes background noise resulting in high-resolution data to be used in prioritising non-coding genetic variants and cell types. Additionally, Avocato allows users to interact with their analysis and prioritisation results interactively in a user-friendly interface. Together this thesis builds on existing technologies, bringing us one step closer to fully understanding GWAS findings and functional studies and elucidating the mechanisms of common human diseases.